Локальные LLM
ext-apps
Official repo for spec & SDK of MCP Apps protocol - standard for UIs embedded AI chatbots, served by MCP servers
★ 2,9 тыс. звёздUnknown
Смотреть руководство →WeKnora — проект с открытым исходным кодом из категории «RAG и базы знаний» от Tencent (Tencent/WeKnora на GitHub). У него 31 891 звезда на GitHub и 4 255 форков, он распространяется по лицензии Unknown и последний раз обновлялся на GitHub 1 октября 2026.
Open-source LLM knowledge platform: turn raw documents into a queryable RAG, an autonomous reasoning agent, and a self-maintaining Wiki.
One binary or app gets you a working local model setup.
Pick a size that fits your RAM/VRAM; smaller models run on laptops.
Use the built-in UI, or point OpenAI-compatible clients at it.
Airplane mode works — your prompts never leave the device.
Large language models drive its smartest features, locally or via API.
Pay nothing per token, ever.
Prompts and documents stay on your hardware, always.
RAG, tools, and agents build on the same base.
Smart compression runs big models on modest hardware.
Works on planes, in basements, behind firewalls.
Download the source and move into the project folder.
git clone https://github.com/Tencent/WeKnora.git
cd WeKnoraEvery project documents its own setup; start with install requirements.
cat README.md | head -60Follow the project's launch command — Docker, pip, npm, or a binary release.
# see README for the exact launch commandIt is an open-source project at Tencent/WeKnora with 4,255 forks. Check the repo's contributors and recent commits to see how active maintenance is.
Licensed under Unknown — check the repo for commercial-use terms.
Yes — with no network calls, there's nothing to leak.
Close on many tasks now; frontier cloud models still lead on hard reasoning.
8B models run on 16GB RAM; bigger models want a real GPU.
Official repo for spec & SDK of MCP Apps protocol - standard for UIs embedded AI chatbots, served by MCP servers
Run frontier AI locally.
Self-hosted rag & knowledge: turn your own pdf textbooks into an ai-searchable knowl.
Microsoft's graph-based RAG: index a corpus into a knowledge graph, then query it with an LLM
OpenKB compiles your documents into a persistent, interlinked wiki knowledge base — no vector DB
MinerU — turns PDFs and Office documents into LLM-ready Markdown and JSON for agents and RAG pipelines, Apache-2.0 plus terms
Другие проекты из категории «RAG и базы знаний» с наибольшим числом звёзд в каталоге LibreAI — реалистичный список альтернатив, если WeKnora вам не подходит.
Build Agentic workflows, RAG pipelines, with rich AI model and tool support on one collaborative workspace. Deploy on cloud, VPC, or…
RAGFlow is a leading open-source Retrieval-Augmented Generation (RAG) engine that fuses cutting-edge RAG with Agent capabilities to create a superior context…
Lightweight OCR toolkit turning PDFs and images into structured, LLM-ready data in 100+ languages
MinerU — turns PDFs and Office documents into LLM-ready Markdown and JSON for agents and RAG pipelines, Apache-2.0 plus terms
Stop renting your intelligence. Own it with AnythingLLM. Everything you need for a powerful local-first agent experience
Соседние проекты из категории «RAG и базы знаний» в каталоге, ближайшие по числу звёзд к WeKnora — продолжайте изучать полку по обе стороны от него.
Microsoft's graph-based RAG: index a corpus into a knowledge graph, then query it with an LLM
A knowledge-base platform with visual AI workflows — build Q&A systems on your own documents
Local knowledge-base RAG and agent app built on LangChain with ChatGLM, Qwen and Llama models
Turn PDFs into AI-ready data: extract Markdown, JSON and tables — and auto-tag for accessibility
PageIndex: vectorless, reasoning-based RAG that indexes documents like a human expert would
Vector similarity search inside Postgres - store embeddings next to your data
Бесплатно и открыто — забирайте исходный код, ставьте звезду репозиторию и начинайте создавать.